A Novel Cluster Kernel RX Algorithm for Anomaly and Change Detection Using Hyperspectral Images

Zhou, Jin; Kwan, Chiman; Ayhan, Bulent; Eismann, Michael T.

doi:10.1109/tgrs.2016.2585495

Cited by 204 publications

(114 citation statements)

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“…Another direction is to apply the pansharpened images to different applications such as target detection [11][12][13], border monitoring [24,25], and anomaly detection [14][15][16][17][18][19][20][21][22]62]. …”

Section: Group Methodsmentioning

confidence: 99%

“…Although the above imagers have resolution good enough for their respective missions, many other applications such as drought monitoring, fire damage assessment, etc., require higher resolutions. Other notable applications of multispectral and hyperspectral images include target detection [6][7][8][9][10][11][12][13], anomaly and change detection [14][15][16][17][18][19][20][21][22][23], tunnel monitoring [24,25], and Mars exploration [26,27].…”

Section: Introductionmentioning

confidence: 99%

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A New Pansharpening Approach for Hyperspectral Images

Kwan¹,

Zhou²,

Budavari³

2018

Colorimetry and Image Processing

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Section: Group Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Pansharpening Approach for Hyperspectral Images

Kwan¹,

Zhou²,

Budavari³

2018

Colorimetry and Image Processing

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“…MS and HS images have been widely used in fire damage assessment [5], anomaly detection [6][7][8][9][10], chemical agent detection and classification [11], border monitoring [12], target detection [13][14][15], and change detection [16,17]. Due to cost considerations, different imagers need to trade off among spatial, spectral, and temporal resolutions.…”

Section: Introductionmentioning

confidence: 99%

Methods and Challenges Using Multispectral and Hyperspectral Images for Practical Change Detection Applications

Kwan

2019

Information

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Multispectral (MS) and hyperspectral (HS) images have been successfully and widely used in remote sensing applications such as target detection, change detection, and anomaly detection. In this paper, we aim at reviewing recent change detection papers and raising some challenges and opportunities in the field from a practitioner’s viewpoint using MS and HS images. For example, can we perform change detection using synthetic hyperspectral images? Can we use temporally-fused images to perform change detection? Some of these areas are ongoing and will require more research attention in the coming years. Moreover, in order to understand the context of our paper, some recent and representative algorithms in change detection using MS and HS images are included, and their advantages and disadvantages will be highlighted.

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“…These methods extract features based on the inflexible rectangular windows, which could cause the high false alarms under complex conditions with various irregular clutters. Some anomaly detection methods such as the cluster kernel Reed-Xiaoli (CKRX) algorithm [57] were also proposed for small target detection, yet they are sensitive to abnormal background pixels. Lately, a novel approach via modified random walks (MRW) [58] was proposed to detect the small IR targets with low signal-to-noise ratio.…”

Section: Introductionmentioning

confidence: 99%

Structure-Adaptive Clutter Suppression for Infrared Small Target Detection: Chain-Growth Filtering

Huang

Liu

et al. 2019

Remote Sensing

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Robust detection of infrared small target is an important and challenging task in many photoelectric detection systems. Using the difference of a specific feature between the target and the background, various detection methods were proposed in recent decades. However, most methods extract the feature in a region with fixed shape, especially in a rectangular region, which causes a problem: when faced with complex-shape clutters, the rectangular region involves the pixels inside and outside the clutters, and the significant grey-level difference among these pixels leads to a relatively large feature in the clutter area, interfering with the target detection. In this paper, we propose a structure-adaptive clutter suppression method, called chain-growth filtering, for robust infrared small target detection. The well-designed filtering model can adjust its shape to fit various clutter structures such as lines, curves and irregular edges, and thus has a more robust clutter suppression capability than the fixed-shape feature extraction strategy. In addition, the proposed method achieves a considerable anti-noise ability by employing guided filter as a preprocessing approach and enjoys the capability of multi-scale target detection without complex parameter tuning. In the experiment, we evaluate the performance of the detection method through 12 typical infrared scenes which contain different types of clutters. Compared with seven state-of-the-art methods, the proposed method shows the superior clutter-suppression effects for various types of clutters and the excellent detection performance for various scenes.Remote Sens. 2020, 12, 47 2 of 22 decades, and they can be generally grouped into two categories: track-before-detect (TBD)-based methods and detect-before-track (DBT)-based methods.The TBD-based methods, such as pipeline filtering [11], hypothesis testing [12], 3-D matched filtering [13], temporal profile filtering [14], dynamic programming [15] and so on, try to use the grayscale consistency and the trajectory continuity of the targets in consecutive frames so as to discriminate the small targets from noise [16][17][18][19]. These methods are based on two assumptions; one is that the motion model of the target is known, the other is that the background motion is slow. In ideal conditions, both assumptions are satisfied; the energy of the targets is accumulated in adjacent frames, and the difference between targets and noise is increased. As a result, the TBD-based methods perform well in low signal-to-clutter conditions. However, in practical applications, we can hardly acquire the precise motion model of the targets, and the backgrounds can move fast when the infrared detector is in a moving platform. Therefore, both of the two assumptions could fail in real situations, and the performance of the TBD-based methods could degrade significantly [20]. Meanwhile, high time and storage requirements also make these TBD approaches unsuited to large-scale engineering projects [21].Compared with TBD-based methods, DBT-bas...

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A Novel Cluster Kernel RX Algorithm for Anomaly and Change Detection Using Hyperspectral Images

Cited by 204 publications

References 10 publications

A New Pansharpening Approach for Hyperspectral Images

A New Pansharpening Approach for Hyperspectral Images

Methods and Challenges Using Multispectral and Hyperspectral Images for Practical Change Detection Applications

Structure-Adaptive Clutter Suppression for Infrared Small Target Detection: Chain-Growth Filtering

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